Iot Cet.docx

Internet of Things based wearable health startups: A study of demographic challenges to adoption

Guided By :-

Presented By :-

Dr.Shweta Nanda

Amrit Mallick PG20180130

Internet of Things based wearable health startups: A study of demographic challenges to adoption

Abstract: To make the present traditional health care model more sustainable focus of healthcare needs to be envisioned to the integrated health care service model wherein not only the technology behind the remote connected care (Internet of Things) but the strategies to implementation plays a huge role to overcome the challenges of penetration . The Internet of Things (IoT) is a network of physical devices and other items, embedded with electronics, software, sensors, and network connectivity, which enables these objects to collect and exchange data. In addition to cost containment, IoT also allows to keep up with constant innovation and shorter product lifecycles, gather real time data for augmented

intelligence,

and

create

end-to-end

communication

from

wearable/implantable/ingestible diagnostic tools to treatment decisions to ICU monitoring to post-discharge care. This paper focuses on the challenges in the adoption of the IoT based remote healthcare model by various health stakeholders. The shared learning from the domains of industry/startups, physicians/hospitals, IOT policy framework and a study across vivid health research papers, helps to map the physician’s perspective and the key behavioral concepts in the adoption of these devices by prospective customers.

Purpose: The purpose of this paper is to explore the available literature on medical wearable’s devices from Industry, IoT India Congress Report, ASSOCHAM Report, Innovation Industries Conclaves, IEEE and various global researches. Also, it presents forth a strong foundation for researchers/ startups to identify the present market awareness and a suitable framework to overcome the challenges to adoption of these devices. Design/methodology/approach – Exploratory study has been conducted using different keywords to draw a list of relevant research papers on Google Scholar and several online

databases like Springer, IEEE, Elsevier, Emerald etc. On the basis of focus group interviewa logical framework is established which emphasis on the gap between the present awareness levels to adoption. Findings –Identification of adoption factors has been done by performing in-depth interview of Sr. physicians and experts. Various constructs have been captured and validated through mapping of primary and secondary data. Dimensions to adoption challengesof remote connected care- Wearable Technology segment has been explored. The challenges have been tabulated to present a comprehensive picture. Originality/value – The paper is original and holds significance as not much literature is available on Remote Connected Care Medical Wearable Technology in published domain and WT has become an area of keen interest in present times. This paper will give a strong foundation of literature to future researchers who want to pursue their studies in this area.

Keywords: Internet of Things, Integrated health care model, adoption, consumer awareness, challenges.

Introduction: According to Section (h) of the Food, Drug & Cosmetic Act, a medical device is “intended for use in diagnosis of disease or other conditions, or in the cure, mitigation, treatment, or prevention of disease, in man or other animals”. Heath Industry challenge is to utilize the big data collected through a wearable medical device intended for use in the diagnosis of disease or other conditions or in the cure, mitigation, treatment or prevention of disease. The use of this wearable device will not only help to foster strong physician and patient relationships by exchanging real time physiological data but also reduce human intervention. This paper lays a strong foundation to identify the consumer awareness challenges in Indian market for IoT based remote wearable devices as well as challenges faced by the key stakeholders who play a vital role in the development of an integrated remote health care model. The key stakeholders are the customers, physicians as well as the policy

administrators who may belong to the IoT application area or different smart health environments across hospitals, clinics and pharmacies. Wearable health tracking devices are being launched every year. These inventions include tracking bands smart watches, contact lenses , glasses, derma patches, clothing’s and consumable pills for continuous monitoring to name a few. The popular devices in the present market are used for fitness monitoring but the new innovation aims to monitor/alertphysiological parameters critical in cure, mitigation, treatment or prevention of chronic diseases in India. According to the Medcon report (2017),devices used for continuous monitoring of chronic diseases have a unique value proposition because their sensors are capable of monitoring multiple biomarkers, including those associated with diabetes (e.g. trace ketones to signal low insulin), hypertension, and certain lung conditions like breast health, skin health, cardiovascular health, asthma monitoring, nicotine levels, blood glucose levels, bed sore and ulcer prevention due to inactivity during hospitalization. Another domain being explored is in treatment and management of neurological disorders to modify behavior and treat anxiety, depression; monitor and prevent seizure, stroke etc. These devices with blue tooth capabilities collect real time data received through biosensors. Proliferation of these technologies has been relatively higher in developed nations and has only very recently taken off in India. The more commercially available devices in India are limited to smart watches, fitness bands and applications integrated with mobile applications. However, India is poised to become one of the largest markets for wearable medical device technologies in the near future. Some of the most recognizable brands are Fitbit, Garmin, Omron, Apple, Zephyr, Xiomi, but a modest number of Indian startups have also emerged like Cardea Labs. Amongst the vivid pool of wearable health solutions lies a healthcare segment of Wearable Monitoring Systems. WPM systems are a potential solution for addressing some of these challenges by enabling advanced sensors, wearable technology, and secure and effective communication platforms between the clinicians and patients. Baig, M. (2017) The Internet of Things (IoT) is widely used to interconnect the available medical resources like wearable monitoring systems and offer reliable, smart, and effective healthcare services. Health monitoring is one of the paradigms that can use the IoT advantages to improve preventive care and remote diagnostic. Architecture of IoT for healthcare applications collects the data and sends it to the cloud where it is analyzed and processed. Actions based on the analyzed data is sent back to the user by the physician.

According to Abdelgawad, A., Yelamarthi, K. (&) Khattab, A. (2017), remote healthcare has become a vital service with the growing rate of senior citizens. Health monitoring, rehabilitation, and assisted living for the elderly and medically challenged humans is an emerging challenge because they require seamless networking between people, medical instruments, and medical and social service providers. This motivates the need for affordable, low-power, reliable, and wearable devices that will improve the quality of life for many elderlies and physically challenged people. According to Yin, Y., Zeng, Y., Chen, X., Fan, Y (2016) et al., The Internet of Things (IoT) platform offers a promising technology to achieve the aforementioned healthcare services, and can further improve the medical service systems. As per Sullivan, H.T., Sahasrabudhe, S (2017) et al., IoT wearable platforms can be used to collect the needed information of the user and its ambient environment and communicate such information wirelessly, where it is processed or stored for tracking the history of the user . Such a connectivity with external devices and services will allow for taking preventive measure (e.g., upon foreseeing an upcoming heart stroke) or providing immediate care (e.g., when a user falls down and needs help). The objective of this paper is to develop a two level framework in order to identify the behavioral challenges to adoption of the IOT based Medical wearable device:  An Exploratory Study has been done through Qualitative analysis on the primary data gathered from physicians.  A Quantitative analysis has been done toanalyze the relationship between educational qualification, prior experience of WT, affordability status and adaptability to IoT based Medical WT Device.  The results drawn from above have been collated to build a robust foundation to identify the challenges behind the e-health care sustainable model .This model not only eases post aberration care but also reduces the risk stances by real time monitoring /diagnosing the health patterns and generating the alerts to stakeholders based on the threshold limits. Connected care for patients is about experience that provides an ease of access to care. Ability to build their own healthcare record’s .Have a better set of processes and workflows to manage their health and care .Have the ability to find similar background patients and be part of the community oriented to future research.

This section of the paper emphasizes on the adoption challenges, however, the awareness level is measured by the primary data predicted in the research methodology.

Review of Previous Studies Ian Ferguson (2016) in his address in “ Mobile health: the power of wearable’s, sensors and app to transform clinical trials” reviewed that according to International Diabetes Federation estimates in Nov 2013, the number of diabetes sufferers will increase 50% with a cost to the health care industry estimated to be $630 billion. Ferguson further stated that Smartphone initially used to simply make and receive phone calls, is expected to become a gateway that channels a rich set of personal information to and fro from a cloud structure such as server. Topol, E.J. 2010, in the “Consumer movement in Health care” and Kish, L.J. & E.J Topol (2015) in “Unpatients-why patients should own their medical data” explained that many owners of smart phones and wearable sensors are using their devices to automatically track measure their own health, including sleep, vitals, and exercise but soon most routine lab test will likely be obtainable by consumers with Smartphone kits, this will shift the data ownership from healthcare providers to patients. Seram, N. & Dhramakeerthi ,C.(2016) in “Wearable Technology Products: Awareness in Sri Lankan Market” explained the knowledge gap between the customers and Wearable Technology Market where their reduced awareness is dependent on the factors like lack of product experience, low trust level, minimal market influence, Low customer motivation and insufficient influence from marketers are also the reasons. Kotler, P., & Armstrong, G. (2005) in his book “Marketing: an Introduction” explained the Innovation Adoption Model. However, steps in Innovation Adoption model state that pushing the customers from “Awareness” to “Evaluation” can be achieved through the use of marketing tools and strategies devised by the marketers. X.-F. Teng, Y.T. Zhang, C.C. Y. Poon & P. Bonato (2008) in “Wearable medical system for p-health”, explained that in Medical WT, all the measured physiological data are collected by a microcontroller based on the processed data the central controller may either generate a warning message to the caregiver or help detect an early disease. James A. L. (2016) “The Baetylus Theorem—The Central Disconnect Driving Consumer Behavior and Investment Returns in Wearable Technologies” explains that There is a fundamental disconnect in how consumers view wearable sensors and how companies market them; this is called The Baetylus Theorem where people believe (falsely) that by buying a wearable sensor they will receive health benefit; data suggest that this is not the case. This idea is grounded social constructs, psychological theories and marketing approaches. A marketing proposal that fails to recognize The Baetylus

Theorem and how it can be integrated into a business offering has not optimized its competitive advantage. L.-B. Chen et al (2016) in “Wrist Eye: Wrist-Wearable Devices and a System for Supporting Elderly Computer Learners“told that Wearable devices, such as wristbands, smart watches, are gaining in popularity. Into such devices can be embedded a variety of sensors which can give birth to a number of diverse functions. Our team wanted to develop an assisted learning system incorporating a wearable device that would be able monitor _rst-time learners' use of mouse and keyboard and provide their instructors with useable feedback. Gao.Y.; Li. H; Luo. Yan (2015) in "An empirical study of wearable technology acceptance in healthcare" identifies that Consumer’s decision to adopt healthcare wearable technology is affected by factors from technology, health, and privacy perspectives. Specially, fitness device users care more about hedonic motivation, functional congruence, social influence, perceived privacy risk, and perceived vulnerability, but medical device users pay more attention to perceived expectancy, selfefficacy, effort expectancy, and perceived severity. Bloss. R. (2015) in “Wearable sensors bring new benefits to continuous medical monitoring, real time physical activity assessment, baby monitoring and industrial applications" highlighted in practical implications that Doctors will be able to replace one-off tests with continuous monitoring that provides a much better continuous real-time “view” into the patient’s conditions. Wearable monitors will help provide much better medical care in the future. Industrial managers and others will be able to monitor and supervise remotely. He, D., Kumar, N., Chen, J. et al. (2015) in “Robust anonymous authentication protocol for health-care applications using wireless medical sensor networks” stated that as an application of the WSN, the wireless medical sensor network (WMSN) could improve health-care quality and has become important in the modern medical system. In the WMSN, physiological data are collected by sensors deployed in the patient’s body and sent to health professionals’ mobile devices through wireless communication. Then health professionals could get the status of the patient anywhere and anytime. The data collected by sensors are very sensitive and important. The leakage of them could compromise the patient’s privacy and their malicious modification could harm the patient’s health. Therefore, both security and privacy are two important issues in WMSNs

Ivaschenko A., Minaev A. (2014) in the conference paper on “Multi-agent Solution for Adaptive Data Analysis in Sensor Networks at the Intelligent Hospital Ward” was based on wireless network of sensors that are used to collect and process medical data

describing the current patient state. A multi-agent architecture is provided for a sensor network of medical devices, which is able to adaptively react to various events in real time. To implement this solution it is proposed to partially process the data by autonomous medical devices without transmitting it to the server and adapt the sampling intervals on the basis of the non-equidistant time series analysis. The solution is illustrated by simulation results and clinical deployment. Kuptsov D., Nechaev B., Gurtov A. (2012) in “Securing Medical Sensor Network with HIP” discussed their framework which heavily relies on Host Identify Protocol (HIP) [1,2,3]—a protocol proposed to overcome the problem of using IP addresses both for host identification and routing. HIP defines a new cryptographic Host Identity name space, thereby splitting the double meaning of IP addresses. In HIP, Host Identities (HI) are used instead of IP addresses in the transport protocol headers for establishing connections. Prior to communication over HIP, two hosts must establish a HIP association. This process is known as HIP base exchange (BEX) [2] and it consists of four messages transferred between initiator (I) and responder (R). A successful BEX authenticates hosts to each other and generates a Diffie-Hellman shared secret key used in creation of two IPsec Encapsulated Security Payload (ESP) Security Associations (SAs), one for each direction. All subsequent traffic between communicating nodes is encrypted by IPsec. Zhelong Wang, Cong Zhao, SenQiu, (2014) "A system of human vital signs monitoring and activity recognition based on body sensor network", develop a health monitoring system that can measure human vital signs and recognize human activity based on body sensor network (BSN). Through the three collection nodes to collect ECG signals, blood oxygen signals and motion signals it was found that the human monitoring system can simultaneously monitor human ECG, heart rate, pulse rate, SpO2 and recognize human activity. A classifier based on coupled hidden Markov model (CHMM) is adopted to recognize human activity. The average recognition accuracy of CHMM classifier is 94.8 percent, which is higher than some existent methods, such as supported vector machine (SVM), C4.5 decision tree and naive Bayes classifier (NBC).The monitoring system may be used for falling detection, elderly care, postoperative care, rehabilitation training, sports training and other fields in the future.

Research Methodology: STEP 2: A questionnaire was developed using the above behavioral determinants. Primary data was gathered and analyzed to study the behavioral challenges to adoption by prospective customers.

. STEP 2(a): Quantitative Study

A Quantitative analysishas been donetoanalyze the relationship between i) Educational qualification and adaptability to IoT based Medical WT Device ii) Prior experience of WT and adaptability to IoT based Medical WT Device iii) Affordability status and adaptability to IoT based Medical WT Device

i) Since one data (Adaptability) is ordinal and Education range is nominal so Chi Square is used for large sample size. H0- There is no significant relation between the education level and willingness to purchase medical WT. H1: There is a significant relation between the education level and willingness to purchase medical WT.

5 5 16 47 13

Adaptability XII and below1 graduate2 post grad3 post grad above4

4 6 12 31 4

3 3 12 3 0

2 2 19 10 1

1 0 0 1 0

Table1: To find out whether the Education and Adaptability (Purchase Intention) of the MWT is related.

Variable A

1 2 3

Chi-square test statistic =

38.0054

p-value =

0.0002

5 5 16 47

Actual frequencies Variable B 4 6 12 31

Number of:

3 3 12 3

rows = columns =

4

2 2 19 10

1 0 0 1

5

Totals 16 59 92

4

13

5 0.5741 3.7425 1.1207 3.3248 81

1 2 3 4 Totals

Variable A

4

1 2 3 4 Totals

5.0000 7.0054 25.8324 40.2811 7.8811 81.0000

4 0.4376 1.4221 0.8180 0.2595 53

0

1

Variable B 3 1.3380 6.8253 3.9568 1.7514 18

Expected frequencies Variable B 4.0000 3 4.5838 1.5568 16.9027 5.7405 26.3568 8.9514 5.1568 1.7514 53.0000 18.0000

2 2.7676 10.2054 15.9135 3.1135 32.0000

2 0.2129 7.5788 2.1975 1.4347 32

1 0.0865 0.3189 0.4973 0.0973 1.0000

0

18

1

1 0.0865 0.3189 0.5082 0.0973 185

Totals 16.0000 59.0000 92.0000 18.0000 185.0000

Conclusion: Ho – is rejected. H1 is accepted, because with confidence level of 95% p value is .0002. So there is a significant relation between the education level and willingness to purchase medical WT.

In order to identify the correlation between two Ordinal Data that is Prior experience with the device and Purchase Intent, we have used spearman’s rank correlation. Since the value of Rho is negative and quite near to 0. Spearman's rank correlation

Sample Size =

186

Spearman's Rho =

-0.06915

Conclusion: There is minimal correlation between prior device experience and purchase intent.

Adaptability-B

5

4

3

2

1

1

14

9

10

11

1

2

19

10

2

8

0

3

40

25

5

7

0

4

7

8

0

3

0

5

0

0

1

2

0

6

1

1

1

1

0

Affordability-A

Since one data (Adaptability) is ordinal and Affordability range is nominal so Chi Square is used for large sample size. H0- There is no significant relation between the Affordability level and willingness to purchase medical WT. Chi-square test statistic = p-value =

Spearman's rank correlation for benefit over price variable and purchase intent (Q13 and Q19) where rho is slightly positive.

Sample Size = Spearman's Rho =

186

33.7106 0.0282

Number of: rows = columns =

6 5

H1: There is a significant relation between the Affordability level and willingness to purchase medical WT

0.285243

Conclusion:

The objective of the study is to capture insights from both the key health stakeholdersphysicians and customers to create a 2 way adoption challenge framework. Also, while assessing the following determinants from customer’s perspective prior experience with the device does not pose a challenge to adoption . Usefulness, security, Ease of use, performance risk, aberration occurrence, susceptibility, purchase intention and engagement with the device

Data Analysis and Interpretation: we present data analysis of the questionnaire attached in appendix. Appropriate statistical tools are used for descriptive and inferential analysis. The results are presented through tables, charts and graphs as per necessity. All items of measurement are mapped with respect to relevant demographics.

In section 1 we present the sample distribution with respect to demographics to provide a clear insight of sample units. Data is collected against various demographics. Demographics are selected as the study is sensitive to them. Data is collected with respect to Age, Gender, Medical and non-medical field, Income, Knowledge of field of awareness, sources of awareness, health threats, benefit of application and health care segment. The study is sensitive to all these demographics hence a balanced sample is selected to meet the requirements.

Sample Distribution First we present sample distribution of all demographics. Gender is one of the most important demographics. As males and females have different metabolism, different requirements and different type of health issues hence gender becomes an extremely important demographic to study,

Gender

Table-1(Sample Distribution of Gender)

Valid

Frequency

Percent

Valid Percent

Cumulative Percent

Female

136

73.1

73.1

73.1

Male

50

26.9

26.9

100.0

Total

186

100.0

100.0

Out of 186 respondents 73% are females while 27% are males. The sample contains more females. Following figure gives a clear picture of distribution.

Figure -1

Age Age is yet another demographic as the overall opinion changes with respect to age hence we included various age groups for study. The older age groups were very difficult to find but the study is sensitive with increasing age hence the sample is collected accordingly. Following table explains the distribution. Table -2 (Distribution of Age)

Valid

Frequency

Percent

Valid Percent

Cumulative Percent

15-25

53

28.5

28.5

28.5

25-35

20

10.8

10.8

39.2

35-45

19

10.2

10.2

49.5

55-65

21

11.3

11.3

60.8

65-75

35

18.8

18.8

79.6

75 and Above

38

20.4

20.4

100.0

Total

186

100.0

100.0

We can observe that the maximum respondents come from age group 75 and above as we mentioned earlier that the study is sensitive to increasing age hence the sample contains

more units from senior age group. Nearly 40% of data comes from 65 and above age group. Following chart gives a bird’s eye view of distribution.

Figure -2

Level of Education As level of education plays an important role in understanding of technology and its usage hence we have collected sample with respect to different levels of education. Following table represents the sample distribution over education level. Table -3 (Sample Distribution of Education Level)

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

XIIthStandard or Below

17

9.1

9.1

9.1

Graduate

59

31.7

31.7

40.9

Post Graduate

92

49.5

49.5

90.3

Above Post Graduate

18

9.7

9.7

100.0

Total

186

100.0

100.0

We could observe that, nearly 50% of the respondents are post graduate while nearly 30% of them are graduates. The responses are collected from all education groups. Following pie chart gives a clear picture of the distribution.

Figure -3

Medical or Non-Medical Field Field of professional belongingness shall play an important role when it comes to medical wearable. Hence we have collected the data about the medical field as well. In case of medical field all operators are included i.e. medicine distributors, hospital staff, doctors and others. Following table compiles the results..

Table -4 (Are you a Medical Practitioner?)

Valid

Frequency

Percent

Valid Percent

Cumulative Percent

No

161

86.6

86.6

86.6

Yes

25

13.4

13.4

100.0

Total

186

100.0

100.0

We can observe that nearly 86% of respondents are from non-medical field while rest 14% respondents are from medical field. Following chart represents data distribution clearly.

Figure -4

Income Income is yet another demographic that decides the social standing of an individual. Purchase of wearable depends on the income of individual as well hence we collect data from various income groups. Following table gives distribution of income per month in INR. Table -5 (Income per month in INR)

Frequency

Percent

Valid Percent

Cumulative Percent

Less than 30,000

45

24.2

24.2

24.2

30,000 -50,000

39

21.0

21.0

45.2

50,000-1 Lac

77

41.4

41.4

86.6

1Lac-3Lac

18

9.7

9.7

96.2

3 Lac-5Lac

3

1.6

1.6

97.8

5 Lac & Above

4

2.2

2.2

100.0

Valid

Total

186

100.0

100.0

We could see that nearly 86% of data comes from monthly income group from Rs. 30,000 to Rs. 1 lac per month. This income group represents the largest chunk of population hence the representativeness in the sample. Following pie chart further gives a clear picture of income distribution.

Figure -5

Field of Application of Wearable Technology In order to check the awareness of respondents about wearable technology, we ask respondents that in which field of application they have awareness of any wearable technology/ wearable device. Following tables compile the results; Table -6 (Field of Awareness)

Responses

Percent of Cases

N

Percent

Medical Wellness

102

42.0%

55.1%

Sports/ Fitness

103

42.4%

55.7%

Awareness Field

Cloth & Fashion

33

13.6%

17.8%

Security

5

2.1%

2.7%

243

100.0%

131.4%

Total a. Dichotomy group tabulated at value 1.

It can be observed that out of fields of awareness Medical Wellness and Sports/witness are most aware fields of medical wearable. Maximum level of awareness (103 respondents) lies with sports/fitness equipment. The least aware field is security while there is not a single response on implantable wearable. This category remains absent from the responses. Further we tried to identify the difference of awareness with respect to gender.

Table -7 (Field of Awareness with respect to Gender)

Awareness

Total

Medical

Sports/

Cloth &

Wellness

Fitness

Fashion

Security

Female

Count

76

79

14

5

135

Male

Count

26

24

19

0

50

Count

102

103

33

5

185

Gender Total

Percentages and totals are based on respondents. a. Dichotomy group tabulated at value 1.

We could see that the highest awareness of males is with respect to medical wearable while females have highest awareness of medical wearable on sports/fitness. Surprisingly, males have a high awareness about wearable application on cloth and fashion in comparison to women.

Sources of Awareness The awareness about the medical wearable comes from different sources. We wish to find out the most effective source of awareness and hence the question was asked, “What are the sources of awareness of medical wearable. Following table shows the results

Table -8 (Sources of Awareness)

Responses

Awareness Source

Percent of Cases

N

Percent

Internet

119

41.2%

64.3%

Doctors

29

10.0%

15.7%

Sports Goods Showroom

17

5.9%

9.2%

Electronics Showroom

6

2.1%

3.2%

Pharmacy

4

1.4%

2.2%

114

39.4%

61.6%

289

100.0%

156.2%

Advertisement/Public Media Total a. Dichotomy group tabulated at value 1.

We could observe that the internet is highest source of awareness with 63% people mentioning it. While the least informative source is Pharmacy as only 2% respondents marked it as a source of awareness.

Application Benefit

It is important to observe that what different application benefits respondents perceive are. Hence we asked respondents to mark on different device application they can get benefit from. Following table compiles the results.

Table -9 (Application Benefit)

Responses N

Percent

87

47.0%

47.8%

52

28.1%

28.6%

Home Healthcare

40

21.6%

22.0%

Others

6

3.2%

3.3%

185

100.0%

101.6%

Sports and Fitness Remote Patient Application Benefit

Total

Percent of Cases

Monitoring

a. Dichotomy group tabulated at value 1.

We can observe that the highest application benefit lies with sports and fitness as nearly 48% respondents mark that option while home healthcare is selected by only 22% respondents.

Importance of Health Segment

Importance of health care segment is yet another important component of the study and hence we asked respondents to mark most important health care segment for them. Following table compiles the results.

Table -10 (Importance of Health Segment )

Responses N Vital Signal Monitors

Percent 134

67.0%

72.0%

37

18.5%

19.9%

3

1.5%

1.6%

3

1.5%

1.6%

ECG Monitors

11

5.5%

5.9%

Others

12

6.0%

6.5%

200

100.0%

107.5%

Activity Monitors Fetal Devices Importance

Percent of Cases

Neuro-monitoring Devices

Total a. Dichotomy group tabulated at value 1.

We could see the importance of health monitoring segment is highest at vital signal monitoring such as pulse rate, heart rate, blood pressure, Oxygen saturation, respiration rate etc. 72% respondent marked it as important health care segment.

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